ArticleFrontiers in genetics2020
Learning Cell-Type-Specific Gene Regulation Mechanisms by Multi-Attention Based Deep Learning With Regulatory Latent Space.
Article in Frontiers in genetics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Predicting gene expression from histone marks using chromatin deep learning models depends on histone mark function, regulatory distance and cellular states.Nucleic acids research · 2025Article
- Transformer Architecture and Attention Mechanisms in Genome Data Analysis: A Comprehensive Review.Biology · 2023Review
- Characterization of transcriptional enhancers in the chicken genome using CRISPR-mediated activation.Frontiers in genome editing · 2023Article
- Learning the histone codes with large genomic windows and three-dimensional chromatin interactions using transformer.Nature communications · 2022Article
- A benchmark study of deep learning-based multi-omics data fusion methods for cancer.Genome biology · 2022Article
- Knowledge-guided artificial intelligence technologies for decoding complex multiomics interactions in cells.Clinical and experimental pediatrics · 2022Article
- Multimodal deep learning for biomedical data fusion: a review.Briefings in bioinformatics · 2022Review
- Virtual Gene Concept and a Corresponding Pragmatic Research Program in Genetical Data Science.Entropy (Basel, Switzerland) · 2021Review
- Construction of Condition-Specific Gene Regulatory Network Using Kernel Canonical Correlation Analysis.Frontiers in genetics · 2021Article
Corrections and comments
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Authors and funding
4 authors.
Funding
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Abstract
Epigenetic gene regulation is a major control mechanism of gene expression. Most existing methods for modeling control mechanisms of gene expression use only a single epigenetic marker and very few methods are successful in modeling complex mechanisms of gene regulations using multiple epigenetic markers on transcriptional regulation. In this paper, we propose a multi-attention based deep learning model that integrates multiple markers to characterize complex gene regulation mechanisms. In experiments with 18 cell line multi-omics data, our proposed model predicted the gene expression level more accurately than the state-of-the-art model. Moreover, the model successfully revealed cell-type-specific gene expression control mechanisms. Finally, the model was used to identify genes enriched for specific cell types in terms of their functions and epigenetic regulation.
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Registered trials
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